The use of online net banking official sites has been rapidly increased now a days. In online transaction attackers need only little information to steal the private information of bank users and can do any kind of fraudulent activities. One of the major drawbacks of commercial losses in online banking is fraud detected by credit card fraud detection system, which has a significant impact on clients. Fraudulent transactions will be discovered after the transaction is completed in the existing novel privacy models. As a result, in this paper, three level server systems are implemented to partition the intermediate gateway with better security. User details, transaction details and account details are considered as sensitive attributes and stored in separate database. And also data suppression scheme to replace the string and numerical characters into special symbols to overcome the traditional cryptography schemes is implemented. The Quasi-Identifiers are hidden by using Anonymization algorithm so that the transactions can be done efficiently.
{"title":"Enhanced Data Privacy Using Vertical Fragmentation and Data Anonymization Techniques","authors":"R. Sudha, G. Pooja, V. Revathy, S. D. Dilip Kumar","doi":"10.3233/apc210292","DOIUrl":"https://doi.org/10.3233/apc210292","url":null,"abstract":"The use of online net banking official sites has been rapidly increased now a days. In online transaction attackers need only little information to steal the private information of bank users and can do any kind of fraudulent activities. One of the major drawbacks of commercial losses in online banking is fraud detected by credit card fraud detection system, which has a significant impact on clients. Fraudulent transactions will be discovered after the transaction is completed in the existing novel privacy models. As a result, in this paper, three level server systems are implemented to partition the intermediate gateway with better security. User details, transaction details and account details are considered as sensitive attributes and stored in separate database. And also data suppression scheme to replace the string and numerical characters into special symbols to overcome the traditional cryptography schemes is implemented. The Quasi-Identifiers are hidden by using Anonymization algorithm so that the transactions can be done efficiently.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130704628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Jayashree, A. Shivaranjani, S. Suvetha, M. J. Rani, P., Suresha Barani
An ambulance is one of saving many lives by taking the people who need health emergencies. Saving the life of the person is one of the challenging and precious ones. Our key idea is to deliver a patient’s health condition before the victim reaches the hospital in this project. Here we use some biomedical sensors like a heartbeat sensor, temperature sensor, and a respiratory sensor to check the patient health status. There will be a continuous update to the hospital about the patient’s condition through the cloud with the help of the internet of things. The hospitals can also track the ambulance’s live location through the GPS placed in the ambulance where it arrives, and they can know at what time the patient reaches the hospital. With this information, if the patient is in critical condition, the hospital staff can make all the earlier arrangements before the patient arrives at the hospital and saves their lives as soon as possible. Here we use the biometric sensor to know the patient’s information by scanning the patient’s fingerprint. The stored database obtains this information. In cases of accident situations, to avoid legal problems, the patient’s information is sent to the cops through the GSM, and it is also intimated to the patient’s relatives as soon as possible. The parameters which are measured by using biomedical sensors are viewed by doctors using the Blynk app.
{"title":"Smart Ambulance System with Remote Knowledge Communications Through Cloud","authors":"B. Jayashree, A. Shivaranjani, S. Suvetha, M. J. Rani, P., Suresha Barani","doi":"10.3233/apc210260","DOIUrl":"https://doi.org/10.3233/apc210260","url":null,"abstract":"An ambulance is one of saving many lives by taking the people who need health emergencies. Saving the life of the person is one of the challenging and precious ones. Our key idea is to deliver a patient’s health condition before the victim reaches the hospital in this project. Here we use some biomedical sensors like a heartbeat sensor, temperature sensor, and a respiratory sensor to check the patient health status. There will be a continuous update to the hospital about the patient’s condition through the cloud with the help of the internet of things. The hospitals can also track the ambulance’s live location through the GPS placed in the ambulance where it arrives, and they can know at what time the patient reaches the hospital. With this information, if the patient is in critical condition, the hospital staff can make all the earlier arrangements before the patient arrives at the hospital and saves their lives as soon as possible. Here we use the biometric sensor to know the patient’s information by scanning the patient’s fingerprint. The stored database obtains this information. In cases of accident situations, to avoid legal problems, the patient’s information is sent to the cops through the GSM, and it is also intimated to the patient’s relatives as soon as possible. The parameters which are measured by using biomedical sensors are viewed by doctors using the Blynk app.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"95 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114581743","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
B. Mohamed Arafath Rajack, N. Subramanian, N. Arun Pragadesh, R. Suvanesh, S. Vignesh
In this modern world agriculture is one of the major booming sectors around the world. In India around 60 percent of GDP comes from agriculture sector alone. Also, around the world there are many technologies showing up in the field of agriculture. In this paper proposed a technology by means of which potential pest attack in the crops can be detected and the respective pesticide is also sprayed as well. Along with these there is a range of sensor employed in the field connected to the controller that will take the real time values from the field and can be displayed in the respective screen (monitor or mobile screen) by means of technology called IOT (Internet of Things). Raspberry-pi is used as the controller to perform IoT. system is linked with an application called “cain” Which allows us to display various values of sensors in the monitor or in mobile application.
{"title":"Implementation of IoT in Agriculture","authors":"B. Mohamed Arafath Rajack, N. Subramanian, N. Arun Pragadesh, R. Suvanesh, S. Vignesh","doi":"10.3233/apc210258","DOIUrl":"https://doi.org/10.3233/apc210258","url":null,"abstract":"In this modern world agriculture is one of the major booming sectors around the world. In India around 60 percent of GDP comes from agriculture sector alone. Also, around the world there are many technologies showing up in the field of agriculture. In this paper proposed a technology by means of which potential pest attack in the crops can be detected and the respective pesticide is also sprayed as well. Along with these there is a range of sensor employed in the field connected to the controller that will take the real time values from the field and can be displayed in the respective screen (monitor or mobile screen) by means of technology called IOT (Internet of Things). Raspberry-pi is used as the controller to perform IoT. system is linked with an application called “cain” Which allows us to display various values of sensors in the monitor or in mobile application.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114741227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hemang Monga, Jatin Bhutani, Muskan Ahuja, Nikita Maid, H. Pande
Indian Sign Language is one of the most important and widely used forms of communication for people with speaking and hearing impairments. Many people or communities have attempted to create systems that read the sign language symbols and convert the same to text, but text or audio to sign language is still infrequent. This project mainly focuses on developing a translating system consisting of many modules that take English audio and convert the input to English text, which is further parsed to structure grammar representation on which grammar rules of Indian Sign Language are applied. Stop words are removed from the reordered sentence. Since the Indian Sign Language does not support conjugation in words, stemming and lemmatization will transform the provided word into its root or original word. Then all the individual words are checked in a dictionary holding videos of each word. If the system does not find words in the dictionary, then the most suitable synonym replaces them. The system proposed by us is inventive as the current systems are bound to direct conversion of words into Indian Sign Language on-the-other-hand our system aims to convert the sentences in Indian Sign Language grammar and effectively display it to the user.
{"title":"Speech to Indian Sign Language Translator","authors":"Hemang Monga, Jatin Bhutani, Muskan Ahuja, Nikita Maid, H. Pande","doi":"10.3233/apc210172","DOIUrl":"https://doi.org/10.3233/apc210172","url":null,"abstract":"Indian Sign Language is one of the most important and widely used forms of communication for people with speaking and hearing impairments. Many people or communities have attempted to create systems that read the sign language symbols and convert the same to text, but text or audio to sign language is still infrequent. This project mainly focuses on developing a translating system consisting of many modules that take English audio and convert the input to English text, which is further parsed to structure grammar representation on which grammar rules of Indian Sign Language are applied. Stop words are removed from the reordered sentence. Since the Indian Sign Language does not support conjugation in words, stemming and lemmatization will transform the provided word into its root or original word. Then all the individual words are checked in a dictionary holding videos of each word. If the system does not find words in the dictionary, then the most suitable synonym replaces them. The system proposed by us is inventive as the current systems are bound to direct conversion of words into Indian Sign Language on-the-other-hand our system aims to convert the sentences in Indian Sign Language grammar and effectively display it to the user.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"62 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123022108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Pawar, M. A. Jawale, Ravi Kumar Tirandasu, S. Potharaju
High dimensionality is the serious issue in the preprocessing of data mining. Having large number of features in the dataset leads to several complications for classifying an unknown instance. In a initial dataspace there may be redundant and irrelevant features present, which leads to high memory consumption, and confuse the learning model created with those properties of features. Always it is advisable to select the best features and generate the classification model for better accuracy. In this research, we proposed a novel feature selection approach and Symmetrical uncertainty and Correlation Coefficient (SU-CCE) for reducing the high dimensional feature space and increasing the classification accuracy. The experiment is performed on colon cancer microarray dataset which has 2000 features. The proposed method derived 38 best features from it. To measure the strength of proposed method, top 38 features extracted by 4 traditional filter-based methods are compared with various classifiers. After careful investigation of result, the proposed approach is competing with most of the traditional methods.
{"title":"SU-CCE: A Novel Feature Selection Approach for Reducing High Dimensionality","authors":"A. Pawar, M. A. Jawale, Ravi Kumar Tirandasu, S. Potharaju","doi":"10.3233/apc210196","DOIUrl":"https://doi.org/10.3233/apc210196","url":null,"abstract":"High dimensionality is the serious issue in the preprocessing of data mining. Having large number of features in the dataset leads to several complications for classifying an unknown instance. In a initial dataspace there may be redundant and irrelevant features present, which leads to high memory consumption, and confuse the learning model created with those properties of features. Always it is advisable to select the best features and generate the classification model for better accuracy. In this research, we proposed a novel feature selection approach and Symmetrical uncertainty and Correlation Coefficient (SU-CCE) for reducing the high dimensional feature space and increasing the classification accuracy. The experiment is performed on colon cancer microarray dataset which has 2000 features. The proposed method derived 38 best features from it. To measure the strength of proposed method, top 38 features extracted by 4 traditional filter-based methods are compared with various classifiers. After careful investigation of result, the proposed approach is competing with most of the traditional methods.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"122 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine learning enables AI and is used in data analytics to overcome many challenges. Machine learning was the growing method of predicting outcomes based on existing data. The computer learns characteristics from the test implementation, then applies characteristics to an unknown dataset to predict the result. Classification is an essential technique of machine learning which is widely used for forecasting. Some classification techniques predict with adequate accuracy, while others show a small precision. This research investigates a process called machine learning classification, which combines different classifiers to enhance the precision of weak architectures. Experimentation using this tool was conducted using a database on heart disease. The collecting and measuring data method were designed to decide how to use the ensemble methodology to improve predictive accuracy in cardiovascular disease. This paper aims not only to enhance the precision of poor different classifiers but also to apply the algorithm with a neural network to demonstrate its usefulness in predicting disease in its earliest stages. The study results show that various classification algorithmic strategies, such as support vector machines, successfully improve the forecasting ability of poor classifiers and show satisfactory success in recognizing heart attack risk. Using ML classification, a cumulative improvement in the accuracy was obtained for poor classification models. That process efficiency was further improved with the introduction of feature extraction and selection, and the findings show substantial improvements in predictive power.
{"title":"Prediction of Heart Disease Severity Measurment Using Deep Learning Techniques","authors":"R. S. Patil, Mohit Gangwar","doi":"10.3233/apc210245","DOIUrl":"https://doi.org/10.3233/apc210245","url":null,"abstract":"Machine learning enables AI and is used in data analytics to overcome many challenges. Machine learning was the growing method of predicting outcomes based on existing data. The computer learns characteristics from the test implementation, then applies characteristics to an unknown dataset to predict the result. Classification is an essential technique of machine learning which is widely used for forecasting. Some classification techniques predict with adequate accuracy, while others show a small precision. This research investigates a process called machine learning classification, which combines different classifiers to enhance the precision of weak architectures. Experimentation using this tool was conducted using a database on heart disease. The collecting and measuring data method were designed to decide how to use the ensemble methodology to improve predictive accuracy in cardiovascular disease. This paper aims not only to enhance the precision of poor different classifiers but also to apply the algorithm with a neural network to demonstrate its usefulness in predicting disease in its earliest stages. The study results show that various classification algorithmic strategies, such as support vector machines, successfully improve the forecasting ability of poor classifiers and show satisfactory success in recognizing heart attack risk. Using ML classification, a cumulative improvement in the accuracy was obtained for poor classification models. That process efficiency was further improved with the introduction of feature extraction and selection, and the findings show substantial improvements in predictive power.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"65 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131573276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
S. Sudhakar, A. Akashwar, M. Ajay Someshwar, T. Dhaneshguru, M. Prem Kumar
The growing network traffic rate in wireless communication demands extended network capacity. Current crypto core methodologies are already reaching the maximum achievable network capacity limits. The combination of AES with other crypto cores and inventing new optimization models have emerged. In this paper, some of the prominent issues related to the existing AES core system, namely, lack of data rate, design complexity, reliability, and discriminative properties. In addition to that, this work also proposes a biometric key generation for AES core that constitutes simpler arithmetic such as substitution, modulo operation, and cyclic shifting for diffusion and confusion metrics which explore cipher transformation level. It is proved that in AES as compared to all other functions S-Box component directly influences the overall system performance both in terms of power consumption overhead, security measures, and path delay, etc.
{"title":"Improving Security Using Modified S-Box for AES Cryptographic Primitives","authors":"S. Sudhakar, A. Akashwar, M. Ajay Someshwar, T. Dhaneshguru, M. Prem Kumar","doi":"10.3233/apc210288","DOIUrl":"https://doi.org/10.3233/apc210288","url":null,"abstract":"The growing network traffic rate in wireless communication demands extended network capacity. Current crypto core methodologies are already reaching the maximum achievable network capacity limits. The combination of AES with other crypto cores and inventing new optimization models have emerged. In this paper, some of the prominent issues related to the existing AES core system, namely, lack of data rate, design complexity, reliability, and discriminative properties. In addition to that, this work also proposes a biometric key generation for AES core that constitutes simpler arithmetic such as substitution, modulo operation, and cyclic shifting for diffusion and confusion metrics which explore cipher transformation level. It is proved that in AES as compared to all other functions S-Box component directly influences the overall system performance both in terms of power consumption overhead, security measures, and path delay, etc.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130551409","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Adnan Telwala, Ayush Pratap, Ketan Gaikwad, Tushar Chaudhari, S. Bhingarkar
Numerous online business sites empower the customers to create a product reviews along with feedback in the shape of ratings. This gives the organization work force a sign about their items’ remaining on the lookout, while likewise empowering individual customer to frame an assessment and help buy an item. As of late, Sentiment Analysis (SA) has gotten quite possibly interesting due to the potential business advantages of text analysis. One of the most important problems in confronting SA is the manner by which to remove feelings in the assessment, as well as how to identify counterfeit good reviews and negative surveys derived from assessment surveys. Besides, the assessment surveys acquired from clients can divided into two categories: positive and negative, which can be utilized by a shopper to choose an item. In this survey, we have thoroughly discussed about fake review detection of products as well as product rating by different SA techniques. Further, we have discussed the research direction in fake review detection and product rating.
{"title":"A Survey on Different Techniques for Product Fake Review Detection and Product Rating","authors":"Adnan Telwala, Ayush Pratap, Ketan Gaikwad, Tushar Chaudhari, S. Bhingarkar","doi":"10.3233/apc210207","DOIUrl":"https://doi.org/10.3233/apc210207","url":null,"abstract":"Numerous online business sites empower the customers to create a product reviews along with feedback in the shape of ratings. This gives the organization work force a sign about their items’ remaining on the lookout, while likewise empowering individual customer to frame an assessment and help buy an item. As of late, Sentiment Analysis (SA) has gotten quite possibly interesting due to the potential business advantages of text analysis. One of the most important problems in confronting SA is the manner by which to remove feelings in the assessment, as well as how to identify counterfeit good reviews and negative surveys derived from assessment surveys. Besides, the assessment surveys acquired from clients can divided into two categories: positive and negative, which can be utilized by a shopper to choose an item. In this survey, we have thoroughly discussed about fake review detection of products as well as product rating by different SA techniques. Further, we have discussed the research direction in fake review detection and product rating.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121756221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
An efficient driver assistance system is essential to avoid mishaps. The collision between the vehicles and objects before vehicle is the one of the principle reason of mishaps that outcomes in terms of diminished safety and higher monetary loss. Researchers are interminably attempting to upgrade the safety means for diminishing the mishap rates. This paper proposes an accurate and proficient technique for identifying objects in front of vehicles utilizing thermal imaging framework. For this purpose, image dataset is obtained with the help of a night vision IR camera. This strategy presents deep network based procedure for recognition of objects in thermal images. The deep network gives the model understanding of real world objects and empowers the object recognition. The real time thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is used to adequately identify objects in real time thermal images. This work can be an incredible help for driver assistance framework. The outcomes exhibits that the proposed work assists to boost public safety with good accuracy.
{"title":"Deep Learning Based Object Recognition in Real Time Images Using Thermal Imaging System","authors":"Rohini Goel, Avinash Sharma, Rajiv Kapoor","doi":"10.3233/apc210215","DOIUrl":"https://doi.org/10.3233/apc210215","url":null,"abstract":"An efficient driver assistance system is essential to avoid mishaps. The collision between the vehicles and objects before vehicle is the one of the principle reason of mishaps that outcomes in terms of diminished safety and higher monetary loss. Researchers are interminably attempting to upgrade the safety means for diminishing the mishap rates. This paper proposes an accurate and proficient technique for identifying objects in front of vehicles utilizing thermal imaging framework. For this purpose, image dataset is obtained with the help of a night vision IR camera. This strategy presents deep network based procedure for recognition of objects in thermal images. The deep network gives the model understanding of real world objects and empowers the object recognition. The real time thermal image database is utilized for the training and validation of deep network. In this work, Faster R-CNN is used to adequately identify objects in real time thermal images. This work can be an incredible help for driver assistance framework. The outcomes exhibits that the proposed work assists to boost public safety with good accuracy.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128128286","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
K. Lakshmi Narayanan, R. Niranjana, E. Francy Irudaya Rani, N. Subbulakshmi, R. Santhana Krishnan
Brain tumour detection is an evergreen topic to attract attention in the examination field of Information Technology innovation with biomedical designing, in view of the gigantic need of proficient and viable strategy for assessment of enormous measure of information. Image segmentation is considered as one of the most vital systems for visualizing tissues in an individual. To robotize image segmentation, we have proposed a calculation to get global optimal thresholding esteem for a specific brain MRI image, utilizing OTSU+Sauvola binarization strategy. The fundamental reason for feature collection is to diminish the quantity of structures utilized in classification while keeping up satisfactory classification exactness. One of the most extra-customary procedures applied for feature extraction is Discrete Wavelet Transform (DWT). Adequately it anticipates the estimation space on a plane to such an extent that the fluctuation of the information is ideally protected. We propose a justifiable model for brain tumours discovery and classification i.e., to classify whether the tumour is benign or malignant, utilizing SVM classification. SVM utilized here deals with basic hazard minimization to group the images for the tumour extraction, and a Graphical User Interface is created for the tumour classification operation, using the MATLAB platform.
{"title":"Powerful and Novel Tumour Detection in Brain MRI Images Employing Hybrid Computational Techniques","authors":"K. Lakshmi Narayanan, R. Niranjana, E. Francy Irudaya Rani, N. Subbulakshmi, R. Santhana Krishnan","doi":"10.3233/apc210279","DOIUrl":"https://doi.org/10.3233/apc210279","url":null,"abstract":"Brain tumour detection is an evergreen topic to attract attention in the examination field of Information Technology innovation with biomedical designing, in view of the gigantic need of proficient and viable strategy for assessment of enormous measure of information. Image segmentation is considered as one of the most vital systems for visualizing tissues in an individual. To robotize image segmentation, we have proposed a calculation to get global optimal thresholding esteem for a specific brain MRI image, utilizing OTSU+Sauvola binarization strategy. The fundamental reason for feature collection is to diminish the quantity of structures utilized in classification while keeping up satisfactory classification exactness. One of the most extra-customary procedures applied for feature extraction is Discrete Wavelet Transform (DWT). Adequately it anticipates the estimation space on a plane to such an extent that the fluctuation of the information is ideally protected. We propose a justifiable model for brain tumours discovery and classification i.e., to classify whether the tumour is benign or malignant, utilizing SVM classification. SVM utilized here deals with basic hazard minimization to group the images for the tumour extraction, and a Graphical User Interface is created for the tumour classification operation, using the MATLAB platform.","PeriodicalId":429440,"journal":{"name":"Recent Trends in Intensive Computing","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126836656","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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